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Question: DESeq2: interaction term as in limma
1
gravatar for sbcn
17 months ago by
sbcn60
Spain
sbcn60 wrote:

Hi,

In limma's documentation, the interaction term is described as:
"The first of these questions relates to the WT.S vs WT.U comparison and the second to Mu.S vs Mu.U . The third relates to the difference of differences, i.e., (Mu.S-Mu.U)-(WT.S-WT.U) , which is called the interaction term."

I want to perform the same analysis using DESeq2 (on RNA-seq data).
I have 2 cell types: A and B
for each cell type, I have a KO of a protein and a control (no KO)
 
I looked at the vignette and at ?results, but I have some doubt regarding what would be equivalent to limma's interaction term (A_ko-A_control)-(B_ko-B_control)

Could someone tell me if the following attempt is correct:

dds <- makeExampleDESeqDataSet(n=100,m=12)

dds$celltype <- factor(c(rep("A",6), rep("B", 6)))
dds$protein <- factor(rep(c(rep("ko",3), rep("control", 3)), 2))

design(dds) <- ~ celltype + protein + celltype:protein

dds <- DESeq(dds)
#resultsNames(dds)

res <- results(dds, contrast=list( c("protein_ko_vs_control","celltypeB.proteinko") ))

Thanks a lot!

 

ADD COMMENTlink modified 17 months ago by Simon Anders3.5k • written 17 months ago by sbcn60
4
gravatar for Simon Anders
17 months ago by
Simon Anders3.5k
Zentrum für Molekularbiologie, Universität Heidelberg
Simon Anders3.5k wrote:

Nearly correct.

The last line should be:

res <- results( dds, name="celltypeB.proteinko" )

 

Have a look at resultsNames:

> resultsNames(dds)
[1] "Intercept"             "celltype_B_vs_A"       "protein_ko_vs_control"
[4] "celltypeB.proteinko"  

 

Celltype_B_vs_A is the difference between the cell lines A and B in control (B_ctrl - A_ctrl), and protein_ko_vs_control is the difference between the ko and ctrl in cell line A (A_ko - A_ctrl). The interaction term, celltypeB.proteinko, is the difference between the effect of the knockdown in cell line B over that in A, i.e., (B_ko - B_ctrl) - (A_ko - A_ctrl), which is what you want.

What you looked at ist the sum the last two terms, i.e., which turns out to be (B_ko - B_ctrl).

R's way of naming interaction term is a bit peculiar. Read "celltypeB.proteinko" as "the additional effect that the knockout has if done on cell line B rather than A", or, equivalently, "the additional part of the difference between A and B, when assessed in ko rather than ctrl".

 

 

ADD COMMENTlink written 17 months ago by Simon Anders3.5k

Thanks a lot Simon for your prompt answer!

It is a lot more clear now to me what each term means.

If a gene is differentially expressed in B_ko - B_ctrl but not in A_ko - A_ctrl (change in expression triggered by the KO in cell line B but not in cell line A), it will come out of the interaction analysis also as significant, correct?

 

ADD REPLYlink written 17 months ago by sbcn60
3

Not necessarily, because "absence of evidence is not evidence of absence" of an effect.

So, if the p value for (B_ko-B_ctrl) is high, this could either mean that there is no effect or that the variance is too high to tell. Hence, seeing a significant p value for (A_ko-A_ctrl) but not for (B_ko-B_ctrl) does not imply that there is a difference between (A_ko-A_ctrl) and (B_ko-B_ctrl).

This is precisely the reason why you have to test for significance of interaction and why it would be incorrect to simply run the analysis independently on the A and the B samples and look at the genes appearing only in one of the two results lists.

ADD REPLYlink written 17 months ago by Simon Anders3.5k

Thank you Simon for the clarification. Then the interaction is indeed what I am looking for.

Best,

 

ADD REPLYlink written 17 months ago by sbcn60
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